code: def validate_epoch(dataloader, model, loss_fn):
      num_batches = len(dataloader)
      model.eval()
      loss = 0
      with torch.no_grad():
          for X, y in dataloader:
              pred = model(X)
              loss += loss_fn(pred, y).item()
      loss /= num_batches
      import copy
  
      model_copy = copy.deepcopy(model)
      return model_copy.cpu().state_dict(), loss
  
file_path: ray/python/ray/train/examples/pytorch/torch_linear_example.py
imports: import argparse
  import os
  import tempfile
  import numpy
  import torch
  import torch.nn
  import ray.train
  from ray.train import Checkpoint, RunConfig, ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 2 for calling a global function with example usage: validate_epoch(dataloader, model, loss_fn) and returns: model_copy,  loss